80
个编辑
更改
→中心性指数的定义与特性Definition and characterization of centrality indices
When centralities are categorized by their approach to cohesiveness, it becomes apparent that the majority of centralities inhabit one category. The count of the number of walks starting from a given vertex differs only in how walks are defined and counted. Restricting consideration to this group allows for a soft characterization which places centralities on a spectrum from walks of length one (degree centrality) to infinite walks (eigenvalue centrality). The observation that many centralities share this familial relationships perhaps explains the high rank correlations between these indices.
When centralities are categorized by their approach to cohesiveness, it becomes apparent that the majority of centralities inhabit one category. The count of the number of walks starting from a given vertex differs only in how walks are defined and counted. Restricting consideration to this group allows for a soft characterization which places centralities on a spectrum from walks of length one (degree centrality) to infinite walks (eigenvalue centrality). The observation that many centralities share this familial relationships perhaps explains the high rank correlations between these indices.
当根据内聚力方法对中心性进行分类时，很明显大多数中心性都将被划分于同一类别。起始于给定顶点的步数总和仅取决于步数的定义以及计数方式。这种分类方式的不足表现为它仅能较弱的描绘中心性特征，即按照一步步度（'''<font color="#ff8000">度中心性 degree centrality</font>'''）到无穷步步长（'''<font color="#ff8000">特征向量中心性 eigenvalue centrality</font>'''）的方式将中心性置于一种光谱状的分类中。对于采用类似方式定义的各种中心性的观察也表明了这些指数之间的高阶相关性。
当根据内聚力方法对中心性进行分类时，很明显大多数中心性都将被划分于同一类别。起始于给定顶点的步数总和仅取决于步数的定义以及计数方式。这种分类方式的不足表现为它仅能较弱的描绘中心性特征，即按照一步步度（'''<font color="#ff8000">度中心性 degree centrality</font>'''）到无穷步步长（'''<font color="#ff8000">特征向量中心性 eigenvalue centrality</font>'''）的方式将中心性置于一种光谱状的分类中。<ref name=Bonacich1987/><ref name="Benzi2013">{{cite journal | last1=Benzi | first1=Michele | last2=Klymko| first2=Christine | year=2013 |title= A matrix analysis of different centrality measures |arxiv=1312.6722 | doi=10.1137/130950550 | volume=36 | issue=2 | journal=SIAM Journal on Matrix Analysis and Applications | pages=686–706}}</ref> 对于采用类似方式定义的各种中心性的观察也表明了这些指数之间的高阶相关性。
===Characterization by network flows==
===Characterization by network flows==